def query(self, watch_key, time_indices=None, slicing=None, mapping=None):
"""Query tensor store for a given watch_key.
Args:
watch_key: The watch key to query.
time_indices: A numpy-style slicing string for time indices. E.g.,
`-1`, `:-2`, `[::2]`. If not provided (`None`), will use -1.
slicing: A numpy-style slicing string for individual time steps.
mapping: An mapping string or a list of them. Supported mappings:
`{None, 'image/png', 'health-pill'}`.
Returns:
The potentially sliced values as a nested list of values or its mapped
format. A `list` of nested `list` of values.
Raises:
ValueError: If the shape of the sliced array is incompatible with mapping
mode. Or if the mapping type is invalid.
"""
if watch_key not in self._tensor_data:
raise KeyError("watch_key not found: %s" % watch_key)
if time_indices is None:
time_indices = "-1"
time_slicing = tensor_helper.parse_time_indices(time_indices)
all_time_indices = list(range(self._tensor_data[watch_key].num_total()))
sliced_time_indices = all_time_indices[time_slicing]
if not isinstance(sliced_time_indices, list):
sliced_time_indices = [sliced_time_indices]
recombine_and_map = False
step_mapping = mapping
if len(sliced_time_indices) > 1 and mapping not in (None,):
recombine_and_map = True
step_mapping = None
output = []
for index in sliced_time_indices:
value = self._tensor_data[watch_key].query(index)[0]
if value is not None and not isinstance(
value, debug_data.InconvertibleTensorProto
):
output.append(
tensor_helper.array_view(
value, slicing=slicing, mapping=step_mapping
)[2]
)
else:
output.append(None)
if recombine_and_map:
if mapping == "image/png":
output = tensor_helper.array_to_base64_png(output)
elif mapping and mapping != "none":
logger.warn(
"Unsupported mapping mode after recomining time steps: %s",
mapping,
)
return output
def testConvertHealthy2DNestedList(self): x = [[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11], [12, 13, 14, 15]] encoded_x = tensor_helper.array_to_base64_png(x) decoded_x = im_util.decode_png(base64.b64decode(encoded_x)) self.assertEqual((4, 4, 3), decoded_x.shape) decoded_flat = decoded_x.flatten() self.assertEqual(0, np.min(decoded_flat)) self.assertEqual(255, np.max(decoded_flat))
def testConvertHealthy2DArray(self): x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) encoded_x = tensor_helper.array_to_base64_png(x) decoded_x = im_util.decode_png(base64.b64decode(encoded_x)) self.assertEqual((3, 3, 3), decoded_x.shape) decoded_flat = decoded_x.flatten() self.assertEqual(0, np.min(decoded_flat)) self.assertEqual(255, np.max(decoded_flat))
def testConvertHealthy2DNestedList(self):
x = [[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11], [12, 13, 14, 15]]
encoded_x = tensor_helper.array_to_base64_png(x)
decoded_x = im_util.decode_png(base64.b64decode(encoded_x))
self.assertEqual((4, 4, 3), decoded_x.shape)
decoded_flat = decoded_x.flatten()
self.assertEqual(0, np.min(decoded_flat))
self.assertEqual(255, np.max(decoded_flat))
def testConvertHealthy2DArray(self):
x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
encoded_x = tensor_helper.array_to_base64_png(x)
decoded_x = im_util.decode_png(base64.b64decode(encoded_x))
self.assertEqual((3, 3, 3), decoded_x.shape)
decoded_flat = decoded_x.flatten()
self.assertEqual(0, np.min(decoded_flat))
self.assertEqual(255, np.max(decoded_flat))
def query(self,
watch_key,
time_indices=None,
slicing=None,
mapping=None):
"""Query tensor store for a given watch_key.
Args:
watch_key: The watch key to query.
time_indices: A numpy-style slicing string for time indices. E.g.,
`-1`, `:-2`, `[::2]`. If not provided (`None`), will use -1.
slicing: A numpy-style slicing string for individual time steps.
mapping: An mapping string or a list of them. Supported mappings:
`{None, 'image/png', 'health-pill'}`.
Returns:
The potentially sliced values as a nested list of values or its mapped
format. A `list` of nested `list` of values.
Raises:
ValueError: If the shape of the sliced array is incompatible with mapping
mode. Or if the mapping type is invalid.
"""
if watch_key not in self._tensor_data:
raise KeyError("watch_key not found: %s" % watch_key)
if time_indices is None:
time_indices = '-1'
time_slicing = tensor_helper.parse_time_indices(time_indices)
all_time_indices = list(range(self._tensor_data[watch_key].num_total()))
sliced_time_indices = all_time_indices[time_slicing]
if not isinstance(sliced_time_indices, list):
sliced_time_indices = [sliced_time_indices]
recombine_and_map = False
step_mapping = mapping
if len(sliced_time_indices) > 1 and mapping not in (None, ):
recombine_and_map = True
step_mapping = None
output = []
for index in sliced_time_indices:
value = self._tensor_data[watch_key].query(index)[0]
if (value is not None and
not isinstance(value, debug_data.InconvertibleTensorProto)):
output.append(tensor_helper.array_view(
value, slicing=slicing, mapping=step_mapping)[2])
else:
output.append(None)
if recombine_and_map:
if mapping == 'image/png':
output = tensor_helper.array_to_base64_png(output)
elif mapping and mapping != 'none':
tf.logging.warn(
'Unsupported mapping mode after recomining time steps: %s',
mapping)
return output
def testImagePngMappingRaisesExceptionForNonRank2Array(self):
x = np.ones([2, 2, 2])
with six.assertRaisesRegex(
self, ValueError, r"Expected rank-2 array; received rank-3 array"):
tensor_helper.array_to_base64_png(x)
def testImagePngMappingRaisesExceptionForNonRank2Array(self):
x = np.ones([2, 2, 2])
with six.assertRaisesRegex(
self, ValueError,
r"Expected rank-2 array; received rank-3 array"):
tensor_helper.array_to_base64_png(x)
